Local interpretable model-agnostic explanations (LIME) [1]} explains by learning a linear model from a randomly sampled neighborhood around z-score normalized instances. Feature selection is controlled by hyperparameters that limit the total number of features used in approximation, such as the top-\(k\)  largest-magnitude coefficients from a ridge regression model.
